In the particular case of aerobic biological treatment technology, the instrumentation, the control and the automation are key factors when the process must be operated to achieve restricted discharge levels. Nowadays, the dissolved oxygen (DO) is one of the most important parameter to control because of its impact on the biological processes and the energy saving related to aeration. The dissolved oxygen concentration in the aerobic biological treatment process should be sufficiently high to supply enough oxygen to the microorganisms in the sludge, so organic matter can be degraded efficiently. On the other hand, an excessively high DO, which requires a high airflow rate, leads to a high energy consumption and may also deteriorate the sludge quality. Hence, both for economical and process reasons, it is of interest to control the DO.
However, the efficient operation of aerobic treatment process is limited and difficult because it is affected by a variety of physical, chemical, and biological factors. The classical methods (on/off and PID) have largely been used but, due to the non-linear character of the bioprocesses and the lack of available models, the controllers were developed for specific operating and environmental conditions. The most significant advantage of intelligence control is that no precise mathematical model is needed, which can well approach any nonlinear continuous function and overcome the shortcomings of traditional control that over depend on accurate mathematical model. In the present invention an integrated neural-fuzzy process controller is developed to predict and control the aeration performance of an aerobic wastewater treatment Process. With such a hybrid fuzzy control algorithm, the proposed controller may lead to determine the optimal airflow rate over operational time period that could end up saving energy.